Incomplete Data Multi-Source Static Computed Tomography Reconstruction with Diffusion Priors and Implicit Neural Representation

TL;DR

This paper presents a novel multi-source static CT reconstruction method that uses diffusion priors and implicit neural representations to improve image quality from sparse and limited-angle data, reducing radiation and scan time.

Contribution

It introduces a diffusion prior-based framework combined with neural implicit representations for improved incomplete data CT reconstruction, a novel approach in this domain.

Findings

Effective reconstruction from sparse view and limited angle data

Outperforms traditional methods in image quality

Validated through numerical experiments

Abstract

The dose of X-ray radiation and the scanning time are crucial factors in computed tomography (CT) for clinical applications. In this work, we introduce a multi-source static CT imaging system designed to rapidly acquire sparse view and limited angle data in CT imaging, addressing these critical factors. This linear imaging inverse problem is solved by a conditional generation process within the denoising diffusion image reconstruction framework. The noisy volume data sample generated by the reverse time diffusion process is projected onto the affine set to ensure its consistency to the measured data. To enhance the quality of the reconstruction, the 3D phantom's orthogonal space projector is parameterized implicitly by a neural network. Then, a self-supervised learning algorithm is adopted to optimize the implicit neural representation. Through this multistage conditional generation process, we obtain a new approximate posterior sampling strategy for MSCT volume reconstruction. Numerical experiments are implemented with various imaging settings to verify the effectiveness of our methods for incomplete data MSCT volume reconstruction.